{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "718464bc-d3bc-4231-ae00-2a3bff2fab68",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import xgboost as xgb\n",
    "import seaborn as sns \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy as np\n",
    "np.int = int\n",
    "import matplotlib.pyplot as plt\n",
    "#导入各种用于评估模型性能和参数优化的函数和类，例如均方误差（MSE）、均绝对误差（MAE）、决定系数（R2 Score）、解释方差分（explained variance score）等\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score,explained_variance_score,mean_absolute_percentage_error,mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "#导入XGBoost库中的XGBClassifier类，用于建立XGBoost分类模型，并导入plot_importance函数用于特征重要性可视化。\n",
    "from xgboost import XGBClassifier, plot_importance\n",
    "#导入互信息函数\n",
    "from sklearn.feature_selection import mutual_info_regression\n",
    "#导入lasso模型筛选特征变量\n",
    "from sklearn.linear_model import Lasso\n",
    "#导入Scikit-Optimize库中的贝叶斯优化相关的函数和类，用于超参数调优\n",
    "from skopt import gp_minimize\n",
    "from skopt.space import Real, Integer\n",
    "#导入交叉验证函数，用于评估模型性能\n",
    "from sklearn.model_selection import cross_val_score\n",
    "#导入随机森林回归模型\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "plt.rcParams['font.family'] = 'Times New Roman'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50045870-09f1-4bd7-b236-4bafb6c90da6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#df = pd.read_excel('指标.xlsx')\n",
    "d = pd.read_excel(r'D:\\work\\暑期工作\\0606_重新处理\\河南省小麦产量预测\\数据\\Feature.xlsx')\n",
    "dd = pd.read_excel(r'D:\\work\\暑期工作\\0606_重新处理\\河南省小麦产量预测\\数据\\Features.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fc42cdc5-0483-4439-98af-3593081eff03",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "d=d.drop(columns=['Unnamed: 0'])\n",
    "dd=dd.drop(columns=['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "59285029-518e-41c4-8d69-a0f2c2da0cba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>AvgSurfT_inst_chengshu</th>\n",
       "      <th>LST_Day_1km_chousui</th>\n",
       "      <th>LST_Night_1km_fennie</th>\n",
       "      <th>soil_temperature_level_2_kaihua</th>\n",
       "      <th>soil_temperature_level_2_guanjiang</th>\n",
       "      <th>soil_temperature_level_2_shouhuo</th>\n",
       "      <th>soil_temperature_level_3_chousui</th>\n",
       "      <th>soil_temperature_level_3_kaihua</th>\n",
       "      <th>soil_temperature_level_3_guanjiang</th>\n",
       "      <th>...</th>\n",
       "      <th>SVAI_chousui</th>\n",
       "      <th>SVAI_kaihua</th>\n",
       "      <th>WDRVI_bajie</th>\n",
       "      <th>WDRVI_chousui</th>\n",
       "      <th>WDVI_bajie</th>\n",
       "      <th>WDVI_yunsui</th>\n",
       "      <th>WDVI_chousui</th>\n",
       "      <th>WDVI_kaihua</th>\n",
       "      <th>wet_kaihua</th>\n",
       "      <th>亩产</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>410522</td>\n",
       "      <td>301.830072</td>\n",
       "      <td>14770.448120</td>\n",
       "      <td>13653.260039</td>\n",
       "      <td>291.679432</td>\n",
       "      <td>294.485981</td>\n",
       "      <td>301.239969</td>\n",
       "      <td>287.263466</td>\n",
       "      <td>288.780851</td>\n",
       "      <td>290.517675</td>\n",
       "      <td>...</td>\n",
       "      <td>0.509428</td>\n",
       "      <td>0.502203</td>\n",
       "      <td>-0.270294</td>\n",
       "      <td>0.038094</td>\n",
       "      <td>0.312649</td>\n",
       "      <td>0.346212</td>\n",
       "      <td>0.380784</td>\n",
       "      <td>0.355850</td>\n",
       "      <td>-0.090609</td>\n",
       "      <td>7467.000138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>410421</td>\n",
       "      <td>301.881276</td>\n",
       "      <td>14811.998245</td>\n",
       "      <td>13724.830174</td>\n",
       "      <td>291.624652</td>\n",
       "      <td>294.462918</td>\n",
       "      <td>301.229017</td>\n",
       "      <td>287.616328</td>\n",
       "      <td>289.032764</td>\n",
       "      <td>290.784441</td>\n",
       "      <td>...</td>\n",
       "      <td>0.456349</td>\n",
       "      <td>0.444113</td>\n",
       "      <td>-0.220879</td>\n",
       "      <td>0.020097</td>\n",
       "      <td>0.272293</td>\n",
       "      <td>0.321097</td>\n",
       "      <td>0.313054</td>\n",
       "      <td>0.308834</td>\n",
       "      <td>-0.088144</td>\n",
       "      <td>5972.666001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>411726</td>\n",
       "      <td>298.990822</td>\n",
       "      <td>15035.873561</td>\n",
       "      <td>13760.252546</td>\n",
       "      <td>291.141219</td>\n",
       "      <td>293.187955</td>\n",
       "      <td>298.777454</td>\n",
       "      <td>287.204259</td>\n",
       "      <td>288.763693</td>\n",
       "      <td>290.271875</td>\n",
       "      <td>...</td>\n",
       "      <td>0.375751</td>\n",
       "      <td>0.406768</td>\n",
       "      <td>-0.273682</td>\n",
       "      <td>-0.176015</td>\n",
       "      <td>0.255247</td>\n",
       "      <td>0.291260</td>\n",
       "      <td>0.276982</td>\n",
       "      <td>0.277341</td>\n",
       "      <td>-0.107184</td>\n",
       "      <td>5687.791498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>410822</td>\n",
       "      <td>302.326105</td>\n",
       "      <td>14909.855104</td>\n",
       "      <td>13684.031442</td>\n",
       "      <td>291.393057</td>\n",
       "      <td>294.128413</td>\n",
       "      <td>300.920627</td>\n",
       "      <td>286.732262</td>\n",
       "      <td>288.209019</td>\n",
       "      <td>290.136585</td>\n",
       "      <td>...</td>\n",
       "      <td>0.466787</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>-0.305916</td>\n",
       "      <td>0.009030</td>\n",
       "      <td>0.277992</td>\n",
       "      <td>0.313744</td>\n",
       "      <td>0.335973</td>\n",
       "      <td>0.327689</td>\n",
       "      <td>-0.062063</td>\n",
       "      <td>7959.597222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>411082</td>\n",
       "      <td>302.665974</td>\n",
       "      <td>14764.439462</td>\n",
       "      <td>13685.820730</td>\n",
       "      <td>292.812644</td>\n",
       "      <td>295.863324</td>\n",
       "      <td>302.456445</td>\n",
       "      <td>288.378758</td>\n",
       "      <td>289.948855</td>\n",
       "      <td>291.813767</td>\n",
       "      <td>...</td>\n",
       "      <td>0.479437</td>\n",
       "      <td>0.456340</td>\n",
       "      <td>-0.155182</td>\n",
       "      <td>0.027083</td>\n",
       "      <td>0.329961</td>\n",
       "      <td>0.356682</td>\n",
       "      <td>0.342203</td>\n",
       "      <td>0.353976</td>\n",
       "      <td>-0.074036</td>\n",
       "      <td>7662.496889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>411081</td>\n",
       "      <td>302.379726</td>\n",
       "      <td>14864.959928</td>\n",
       "      <td>13716.550674</td>\n",
       "      <td>292.036420</td>\n",
       "      <td>294.927847</td>\n",
       "      <td>301.419777</td>\n",
       "      <td>287.740673</td>\n",
       "      <td>289.256381</td>\n",
       "      <td>291.034992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.438036</td>\n",
       "      <td>0.427608</td>\n",
       "      <td>-0.222630</td>\n",
       "      <td>-0.033628</td>\n",
       "      <td>0.276777</td>\n",
       "      <td>0.307460</td>\n",
       "      <td>0.306785</td>\n",
       "      <td>0.308283</td>\n",
       "      <td>-0.090942</td>\n",
       "      <td>6613.422500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>411424</td>\n",
       "      <td>302.404313</td>\n",
       "      <td>14767.178119</td>\n",
       "      <td>13698.554350</td>\n",
       "      <td>293.142870</td>\n",
       "      <td>296.125191</td>\n",
       "      <td>302.061064</td>\n",
       "      <td>288.739608</td>\n",
       "      <td>290.273988</td>\n",
       "      <td>292.107970</td>\n",
       "      <td>...</td>\n",
       "      <td>0.537977</td>\n",
       "      <td>0.518776</td>\n",
       "      <td>-0.049587</td>\n",
       "      <td>0.143995</td>\n",
       "      <td>0.356591</td>\n",
       "      <td>0.372768</td>\n",
       "      <td>0.380640</td>\n",
       "      <td>0.392420</td>\n",
       "      <td>-0.074186</td>\n",
       "      <td>7579.469809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>411724</td>\n",
       "      <td>300.297286</td>\n",
       "      <td>14800.494981</td>\n",
       "      <td>13753.180467</td>\n",
       "      <td>291.750152</td>\n",
       "      <td>293.718662</td>\n",
       "      <td>299.219180</td>\n",
       "      <td>287.779530</td>\n",
       "      <td>289.413163</td>\n",
       "      <td>290.932713</td>\n",
       "      <td>...</td>\n",
       "      <td>0.511967</td>\n",
       "      <td>0.563379</td>\n",
       "      <td>0.082507</td>\n",
       "      <td>0.149694</td>\n",
       "      <td>0.389395</td>\n",
       "      <td>0.347135</td>\n",
       "      <td>0.345173</td>\n",
       "      <td>0.381115</td>\n",
       "      <td>-0.079914</td>\n",
       "      <td>5754.140236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>411324</td>\n",
       "      <td>300.250438</td>\n",
       "      <td>14870.302144</td>\n",
       "      <td>13755.946153</td>\n",
       "      <td>291.086794</td>\n",
       "      <td>293.369445</td>\n",
       "      <td>300.014684</td>\n",
       "      <td>287.141116</td>\n",
       "      <td>288.584741</td>\n",
       "      <td>290.356689</td>\n",
       "      <td>...</td>\n",
       "      <td>0.480808</td>\n",
       "      <td>0.460888</td>\n",
       "      <td>-0.102704</td>\n",
       "      <td>0.005744</td>\n",
       "      <td>0.309025</td>\n",
       "      <td>0.317261</td>\n",
       "      <td>0.348503</td>\n",
       "      <td>0.307512</td>\n",
       "      <td>-0.097445</td>\n",
       "      <td>5467.128359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>410122</td>\n",
       "      <td>302.101448</td>\n",
       "      <td>14900.316284</td>\n",
       "      <td>13728.293344</td>\n",
       "      <td>292.494994</td>\n",
       "      <td>295.405081</td>\n",
       "      <td>301.660649</td>\n",
       "      <td>288.002027</td>\n",
       "      <td>289.590761</td>\n",
       "      <td>291.427115</td>\n",
       "      <td>...</td>\n",
       "      <td>0.380461</td>\n",
       "      <td>0.390789</td>\n",
       "      <td>-0.356993</td>\n",
       "      <td>-0.172850</td>\n",
       "      <td>0.260427</td>\n",
       "      <td>0.290878</td>\n",
       "      <td>0.283795</td>\n",
       "      <td>0.284526</td>\n",
       "      <td>-0.095178</td>\n",
       "      <td>6637.200003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       NAME  AvgSurfT_inst_chengshu  LST_Day_1km_chousui   \n",
       "0    410522              301.830072         14770.448120  \\\n",
       "1    410421              301.881276         14811.998245   \n",
       "2    411726              298.990822         15035.873561   \n",
       "3    410822              302.326105         14909.855104   \n",
       "4    411082              302.665974         14764.439462   \n",
       "..      ...                     ...                  ...   \n",
       "97   411081              302.379726         14864.959928   \n",
       "98   411424              302.404313         14767.178119   \n",
       "99   411724              300.297286         14800.494981   \n",
       "100  411324              300.250438         14870.302144   \n",
       "101  410122              302.101448         14900.316284   \n",
       "\n",
       "     LST_Night_1km_fennie  soil_temperature_level_2_kaihua   \n",
       "0            13653.260039                       291.679432  \\\n",
       "1            13724.830174                       291.624652   \n",
       "2            13760.252546                       291.141219   \n",
       "3            13684.031442                       291.393057   \n",
       "4            13685.820730                       292.812644   \n",
       "..                    ...                              ...   \n",
       "97           13716.550674                       292.036420   \n",
       "98           13698.554350                       293.142870   \n",
       "99           13753.180467                       291.750152   \n",
       "100          13755.946153                       291.086794   \n",
       "101          13728.293344                       292.494994   \n",
       "\n",
       "     soil_temperature_level_2_guanjiang  soil_temperature_level_2_shouhuo   \n",
       "0                            294.485981                        301.239969  \\\n",
       "1                            294.462918                        301.229017   \n",
       "2                            293.187955                        298.777454   \n",
       "3                            294.128413                        300.920627   \n",
       "4                            295.863324                        302.456445   \n",
       "..                                  ...                               ...   \n",
       "97                           294.927847                        301.419777   \n",
       "98                           296.125191                        302.061064   \n",
       "99                           293.718662                        299.219180   \n",
       "100                          293.369445                        300.014684   \n",
       "101                          295.405081                        301.660649   \n",
       "\n",
       "     soil_temperature_level_3_chousui  soil_temperature_level_3_kaihua   \n",
       "0                          287.263466                       288.780851  \\\n",
       "1                          287.616328                       289.032764   \n",
       "2                          287.204259                       288.763693   \n",
       "3                          286.732262                       288.209019   \n",
       "4                          288.378758                       289.948855   \n",
       "..                                ...                              ...   \n",
       "97                         287.740673                       289.256381   \n",
       "98                         288.739608                       290.273988   \n",
       "99                         287.779530                       289.413163   \n",
       "100                        287.141116                       288.584741   \n",
       "101                        288.002027                       289.590761   \n",
       "\n",
       "     soil_temperature_level_3_guanjiang  ...  SVAI_chousui  SVAI_kaihua   \n",
       "0                            290.517675  ...      0.509428     0.502203  \\\n",
       "1                            290.784441  ...      0.456349     0.444113   \n",
       "2                            290.271875  ...      0.375751     0.406768   \n",
       "3                            290.136585  ...      0.466787     0.392344   \n",
       "4                            291.813767  ...      0.479437     0.456340   \n",
       "..                                  ...  ...           ...          ...   \n",
       "97                           291.034992  ...      0.438036     0.427608   \n",
       "98                           292.107970  ...      0.537977     0.518776   \n",
       "99                           290.932713  ...      0.511967     0.563379   \n",
       "100                          290.356689  ...      0.480808     0.460888   \n",
       "101                          291.427115  ...      0.380461     0.390789   \n",
       "\n",
       "     WDRVI_bajie  WDRVI_chousui  WDVI_bajie  WDVI_yunsui  WDVI_chousui   \n",
       "0      -0.270294       0.038094    0.312649     0.346212      0.380784  \\\n",
       "1      -0.220879       0.020097    0.272293     0.321097      0.313054   \n",
       "2      -0.273682      -0.176015    0.255247     0.291260      0.276982   \n",
       "3      -0.305916       0.009030    0.277992     0.313744      0.335973   \n",
       "4      -0.155182       0.027083    0.329961     0.356682      0.342203   \n",
       "..           ...            ...         ...          ...           ...   \n",
       "97     -0.222630      -0.033628    0.276777     0.307460      0.306785   \n",
       "98     -0.049587       0.143995    0.356591     0.372768      0.380640   \n",
       "99      0.082507       0.149694    0.389395     0.347135      0.345173   \n",
       "100    -0.102704       0.005744    0.309025     0.317261      0.348503   \n",
       "101    -0.356993      -0.172850    0.260427     0.290878      0.283795   \n",
       "\n",
       "     WDVI_kaihua  wet_kaihua           亩产  \n",
       "0       0.355850   -0.090609  7467.000138  \n",
       "1       0.308834   -0.088144  5972.666001  \n",
       "2       0.277341   -0.107184  5687.791498  \n",
       "3       0.327689   -0.062063  7959.597222  \n",
       "4       0.353976   -0.074036  7662.496889  \n",
       "..           ...         ...          ...  \n",
       "97      0.308283   -0.090942  6613.422500  \n",
       "98      0.392420   -0.074186  7579.469809  \n",
       "99      0.381115   -0.079914  5754.140236  \n",
       "100     0.307512   -0.097445  5467.128359  \n",
       "101     0.284526   -0.095178  6637.200003  \n",
       "\n",
       "[102 rows x 66 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75eb75d7-b0ce-4b35-98b5-91a2ab2470aa",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bb20839c-6340-44b1-a9e7-3c0c367273c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除特征的个数： 0\n",
      "出错特征的个数： 0\n",
      "删除的特征： []\n"
     ]
    }
   ],
   "source": [
    "#############删除缺失率高于05的特征\n",
    "null_list = []\n",
    "error_list = []\n",
    "for col in d.columns:\n",
    "    try:\n",
    "        null_list.append([col,d[col].isnull().sum() / d.shape[0]])\n",
    "    except:\n",
    "        error_list.append(col)\n",
    "null_df = pd.DataFrame(null_list,columns = ['特征名称','缺失率'])\n",
    "del_cols = null_df['特征名称'][null_df['缺失率'] >= 0.5]\n",
    "print('删除特征的个数：',len(del_cols))\n",
    "print('出错特征的个数：',len(error_list))\n",
    "print('删除的特征：',del_cols.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aba9ae28-97f8-4fec-bc66-dd5fbfd318ec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除特征的个数： 0\n",
      "出错特征的个数： 0\n",
      "删除的特征： []\n"
     ]
    }
   ],
   "source": [
    "#############表格中用绿色标记表示删除同值比例高的特征\n",
    "tz_list = []\n",
    "error_list = []\n",
    "for col in d.columns:\n",
    "    try:\n",
    "        tz_list.append([col,d[d[col] == d[col].mode()[0]].shape[0] / (d.shape[0] - d[col].isnull().sum())])\n",
    "    except:\n",
    "        error_list.append(col)\n",
    "tz_df = pd.DataFrame(tz_list,columns = ['特征名称','同值比例'])\n",
    "del_cols = tz_df['特征名称'][tz_df['同值比例'] >= 0.9]\n",
    "print('删除特征的个数：',len(del_cols))\n",
    "print('出错特征的个数：',len(error_list))\n",
    "print('删除的特征：',del_cols.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4032c90c-2e9f-47ff-9b43-383d7419a1d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "d=d.drop(columns=['GNDVI_kaihua','NDVI_chousui','OSAVI_chousui',\n",
    "                   'SVAI_chousui','SR_chousui','NAME','DVI_yunsui', \n",
    "                  'EVI_yunsui', 'EVI2_yunsui', 'GCVI_yunsui', 'Lai_500m_yunsui', 'MCARI_yunsui', 'MSR_yunsui', 'NDVI_yunsui', \n",
    "                  'OSAVI_yunsui', 'SVAI_yunsui', 'WDVI_yunsui','NAME','soil_temperature_level_2_shouhuo',\n",
    "                  'VDVI_shouhuo','AvgSurfT_inst_chengshu','soil_temperature_level_2_guanjiang', 'soil_temperature_level_3_guanjiang', \n",
    "                  'soil_temperature_level_1_guanjiang', 'SoilMoi0_10cm_inst_guanjiang', 'SoilMoi10_40cm_inst_guanjiang',\n",
    "                  'soil_temperature_level_2_kaihua', 'soil_temperature_level_3_kaihua', 'SoilMoi0_10cm_inst_kaihua', 'SoilMoi10_40cm_inst_kaihua',\n",
    "                  'DVI_kaihua', 'EVI_kaihua', 'EVI2_kaihua', 'GCVI_kaihua', 'Lai_500m_kaihua', 'MCARI_kaihua', 'OSAVI_kaihua', \n",
    "                  'SR_kaihua', 'SVAI_kaihua', 'WDVI_kaihua', 'wet_kaihua','LST_Day_1km_chousui', 'soil_temperature_level_3_chousui', \n",
    "                  'SoilMoi0_10cm_inst_chousui', 'SoilMoi10_40cm_inst_chousui', 'DVI_chousui', 'EVI_chousui', 'EVI2_chousui', \n",
    "                   'Lai_500m_chousui', 'MCARI_chousui', 'MSR_chousui', 'WDRVI_chousui', 'WDVI_chousui','DVI_yunsui', \n",
    "                  'EVI_yunsui', 'EVI2_yunsui', 'GCVI_yunsui', 'Lai_500m_yunsui', 'MCARI_yunsui', 'MSR_yunsui', 'NDVI_yunsui', \n",
    "                  'OSAVI_yunsui', 'SVAI_yunsui', 'WDVI_yunsui','DVI_bajie', 'EVI_bajie', 'EVI2_bajie', 'GCVI_bajie', 'GNDVI_bajie',\n",
    "                  'Lai_500m_bajie', 'MCARI_bajie', 'MSR_bajie', 'NDVI_bajie', 'OSAVI_bajie', 'WDRVI_bajie', 'WDVI_bajie'])\n",
    "                  \n",
    "dd=dd.drop(columns=['GNDVI_kaihua','NDVI_chousui','OSAVI_chousui',\n",
    "                   'SVAI_chousui','SR_chousui','NAME','DVI_yunsui', \n",
    "                  'EVI_yunsui', 'EVI2_yunsui', 'GCVI_yunsui', 'Lai_500m_yunsui', 'MCARI_yunsui', 'MSR_yunsui', 'NDVI_yunsui', \n",
    "                  'OSAVI_yunsui', 'SVAI_yunsui', 'WDVI_yunsui','NAME','soil_temperature_level_2_shouhuo',\n",
    "                  'VDVI_shouhuo','AvgSurfT_inst_chengshu','soil_temperature_level_2_guanjiang', 'soil_temperature_level_3_guanjiang', \n",
    "                  'soil_temperature_level_1_guanjiang', 'SoilMoi0_10cm_inst_guanjiang', 'SoilMoi10_40cm_inst_guanjiang',\n",
    "                  'soil_temperature_level_2_kaihua', 'soil_temperature_level_3_kaihua', 'SoilMoi0_10cm_inst_kaihua', 'SoilMoi10_40cm_inst_kaihua',\n",
    "                  'DVI_kaihua', 'EVI_kaihua', 'EVI2_kaihua', 'GCVI_kaihua', 'Lai_500m_kaihua', 'MCARI_kaihua', 'OSAVI_kaihua', \n",
    "                  'SR_kaihua', 'SVAI_kaihua', 'WDVI_kaihua', 'wet_kaihua','LST_Day_1km_chousui', 'soil_temperature_level_3_chousui', \n",
    "                  'SoilMoi0_10cm_inst_chousui', 'SoilMoi10_40cm_inst_chousui', 'DVI_chousui', 'EVI_chousui', 'EVI2_chousui', \n",
    "                   'Lai_500m_chousui', 'MCARI_chousui', 'MSR_chousui', 'WDRVI_chousui', 'WDVI_chousui','DVI_yunsui', \n",
    "                  'EVI_yunsui', 'EVI2_yunsui', 'GCVI_yunsui', 'Lai_500m_yunsui', 'MCARI_yunsui', 'MSR_yunsui', 'NDVI_yunsui', \n",
    "                  'OSAVI_yunsui', 'SVAI_yunsui', 'WDVI_yunsui','DVI_bajie', 'EVI_bajie', 'EVI2_bajie', 'GCVI_bajie', 'GNDVI_bajie',\n",
    "                  'Lai_500m_bajie', 'MCARI_bajie', 'MSR_bajie', 'NDVI_bajie', 'OSAVI_bajie', 'WDRVI_bajie', 'WDVI_bajie'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4a117973-8634-4679-8251-f818d42ab8c0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LST_Night_1km_fennie</th>\n",
       "      <th>亩产</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13645.564675</td>\n",
       "      <td>7421.059765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13730.892892</td>\n",
       "      <td>5905.753158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13781.911220</td>\n",
       "      <td>5681.763483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13697.783412</td>\n",
       "      <td>7947.794351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13670.887570</td>\n",
       "      <td>7840.499426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>13761.025977</td>\n",
       "      <td>6179.400354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>13726.494448</td>\n",
       "      <td>7380.460870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>13707.298647</td>\n",
       "      <td>5993.838377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>13820.220001</td>\n",
       "      <td>5321.619139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>13713.055760</td>\n",
       "      <td>5738.673461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>510 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     LST_Night_1km_fennie           亩产\n",
       "0            13645.564675  7421.059765\n",
       "1            13730.892892  5905.753158\n",
       "2            13781.911220  5681.763483\n",
       "3            13697.783412  7947.794351\n",
       "4            13670.887570  7840.499426\n",
       "..                    ...          ...\n",
       "505          13761.025977  6179.400354\n",
       "506          13726.494448  7380.460870\n",
       "507          13707.298647  5993.838377\n",
       "508          13820.220001  5321.619139\n",
       "509          13713.055760  5738.673461\n",
       "\n",
       "[510 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "299d2aaa-7024-4510-a82e-7a8c7e2bce87",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### 全部入模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0fc106ad-358a-451e-bbdf-57729747d20a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train =d.drop(columns = [\"亩产\"])\n",
    "y_train = d['亩产']\n",
    "x_test =dd.drop(columns = [\"亩产\"])\n",
    "y_test = dd['亩产']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef9e4a27-8669-49ea-b699-edba291bb268",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = xgb.XGBRegressor(n_estimators=150,\n",
    "                                 max_depth=5,\n",
    "                                 learning_rate=0.1,\n",
    "                                 \n",
    "                                 random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "44180510-e0eb-449d-bc96-8b985e0ca0c3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "xgboost_model_fit = model.fit(x_train, y_train)\n",
    "y_pred_bo = xgboost_model_fit.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "136d8f93-7a6c-4694-bbb0-cbc45073abd5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>平均绝对误差（MAPE）</th>\n",
       "      <th>平均绝对误差（MAE）</th>\n",
       "      <th>均方根误差（RMSE）</th>\n",
       "      <th>决定系数（R^2）</th>\n",
       "      <th>解释方差</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bo-xgboost</th>\n",
       "      <td>0.164619</td>\n",
       "      <td>950.592518</td>\n",
       "      <td>1183.543812</td>\n",
       "      <td>0.222541</td>\n",
       "      <td>0.223751</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            平均绝对误差（MAPE）  平均绝对误差（MAE）  均方根误差（RMSE）  决定系数（R^2）      解释方差\n",
       "bo-xgboost      0.164619   950.592518  1183.543812   0.222541  0.223751"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 平均绝对百分比误差\n",
    "mape=mean_absolute_percentage_error(y_test,y_pred_bo)\n",
    "# 计算均方根误差（RMSE）\n",
    "rmse = mean_squared_error(y_test, y_pred_bo, squared=False)\n",
    "# 计算平均绝对误差（MAE）\n",
    "mae = mean_absolute_error(y_test, y_pred_bo)\n",
    "# 计算决定系数（R^2）\n",
    "r2 = r2_score(y_test, y_pred_bo)\n",
    "# 计算解释方差分\n",
    "explained_var = explained_variance_score(y_test, y_pred_bo)\n",
    "\n",
    "results1 = pd.DataFrame()\n",
    "results1['平均绝对误差（MAPE）'] = [mape]\n",
    "results1['平均绝对误差（MAE）'] = [mae]\n",
    "results1['均方根误差（RMSE）'] = [rmse]\n",
    "results1['决定系数（R^2）'] = [r2]\n",
    "results1['解释方差'] = [explained_var]\n",
    "results1.index = ['xgboost']\n",
    "results1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "43a37742-b0a1-4ac8-9f09-39d1fe7992bf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_test</th>\n",
       "      <th>y_pred</th>\n",
       "      <th>precent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7467.000138</td>\n",
       "      <td>7261.023926</td>\n",
       "      <td>-2.76%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5972.666001</td>\n",
       "      <td>5804.768066</td>\n",
       "      <td>-2.81%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5687.791498</td>\n",
       "      <td>5356.045410</td>\n",
       "      <td>-5.83%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7959.597222</td>\n",
       "      <td>7263.558594</td>\n",
       "      <td>-8.74%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7662.496889</td>\n",
       "      <td>7263.558594</td>\n",
       "      <td>-5.21%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6613.422500</td>\n",
       "      <td>5478.551758</td>\n",
       "      <td>-17.16%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>7579.469809</td>\n",
       "      <td>4906.352051</td>\n",
       "      <td>-35.27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5754.140236</td>\n",
       "      <td>5767.611328</td>\n",
       "      <td>0.23%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>5467.128359</td>\n",
       "      <td>6252.513184</td>\n",
       "      <td>14.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>6637.200003</td>\n",
       "      <td>7294.502930</td>\n",
       "      <td>9.90%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          y_test       y_pred  precent\n",
       "0    7467.000138  7261.023926   -2.76%\n",
       "1    5972.666001  5804.768066   -2.81%\n",
       "2    5687.791498  5356.045410   -5.83%\n",
       "3    7959.597222  7263.558594   -8.74%\n",
       "4    7662.496889  7263.558594   -5.21%\n",
       "..           ...          ...      ...\n",
       "97   6613.422500  5478.551758  -17.16%\n",
       "98   7579.469809  4906.352051  -35.27%\n",
       "99   5754.140236  5767.611328    0.23%\n",
       "100  5467.128359  6252.513184   14.37%\n",
       "101  6637.200003  7294.502930    9.90%\n",
       "\n",
       "[102 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_array = np.array(y_test)\n",
    "\n",
    "y_pred_series = pd.Series(y_pred_bo, name='y_pred')\n",
    "y_test_series = pd.Series(y_test_array, name='y_test')\n",
    "dftest= pd.concat([y_test_series, y_pred_series], axis=1)\n",
    "dftest['precent'] = ((dftest['y_pred'] - dftest['y_test']) / dftest['y_test']) * 100\n",
    "dftest['precent'] = dftest['precent'].apply(lambda x: '{:.2f}%'.format(x))\n",
    "dftest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b1994e2-af3b-478a-992d-e4128e188f91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test_sorted = np.sort(y_test)\n",
    "y_pred_sorted = y_pred_bo[np.argsort(y_test)]\n",
    "\n",
    "# 绘制折线对比图\n",
    "plt.plot(range(len(y_test_sorted)), y_test_sorted, label='Actual')\n",
    "plt.plot(range(len(y_pred_sorted)), y_pred_sorted, label='Predicted',linestyle='--')\n",
    "#plt.xlabel('Index')\n",
    "plt.ylabel('Value',fontsize=16)\n",
    "plt.title('Actual vs. Predicted (Bo-XGBoost)',fontsize=16)\n",
    "plt.legend()\n",
    "plt.ylim(1000, 10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "df4a379a-10bb-4f49-8b5f-1490cbda5d25",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(y_test, y_pred_bo, alpha=0.5, label='Data Points')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.title('bh-XGBoost Scatter Plot of Actual vs. Predicted Values')\n",
    "\n",
    "# 添加对角线\n",
    "max_value = max(max(y_test), max(y_pred_bo))\n",
    "min_value = min(min(y_test), min(y_pred_bo))\n",
    "plt.plot([min_value, max_value], [min_value, max_value], color='red', linestyle='--', linewidth=2, label='Perfect Prediction')\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3896133d-f939-4563-8d25-5aed0e0b5413",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
